Suppr超能文献

用于域级 DNA 链置换系统的动力学和热力学的自动化序列水平分析。

Automated sequence-level analysis of kinetics and thermodynamics for domain-level DNA strand-displacement systems.

机构信息

California Institute of Technology , Pasadena, CA , USA.

出版信息

J R Soc Interface. 2018 Dec 21;15(149):20180107. doi: 10.1098/rsif.2018.0107.

Abstract

As an engineering material, DNA is well suited for the construction of biochemical circuits and systems, because it is simple enough that its interactions can be rationally designed using Watson-Crick base pairing rules, yet the design space is remarkably rich. When designing DNA systems, this simplicity permits using functional sections of each strand, called domains, without considering particular nucleotide sequences. However, the actual sequences used may have interactions not predicted at the domain-level abstraction, and new rigorous analysis techniques are needed to determine the extent to which the chosen sequences conform to the system's domain-level description. We have developed a computational method for verifying sequence-level systems by identifying discrepancies between the domain-level and sequence-level behaviour. This method takes a DNA system, as specified using the domain-level tool Peppercorn, and analyses data from the stochastic sequence-level simulator Multistrand and sequence-level thermodynamic analysis tool NUPACK to estimate important aspects of the system, such as reaction rate constants and secondary structure formation. These techniques, implemented as the Python package KinDA, will allow researchers to predict the kinetic and thermodynamic behaviour of domain-level systems after sequence assignment, as well as to detect violations of the intended behaviour.

摘要

作为一种工程材料,DNA 非常适合构建生化电路和系统,因为它足够简单,可以使用沃森-克里克碱基配对规则进行合理设计,而设计空间却非常丰富。在设计 DNA 系统时,这种简单性允许使用每条链的功能部分(称为域),而无需考虑特定的核苷酸序列。然而,实际使用的序列可能具有在域级抽象中未预测到的相互作用,并且需要新的严格分析技术来确定所选序列与系统的域级描述的符合程度。我们已经开发了一种通过识别域级和序列级行为之间的差异来验证序列级系统的计算方法。该方法采用使用域级工具 Peppercorn 指定的 DNA 系统,并分析来自随机序列级模拟器 Multistrand 和序列级热力学分析工具 NUPACK 的数据,以估计系统的重要方面,例如反应速率常数和二级结构形成。这些技术作为 Python 包 KinDA 实现,将允许研究人员在进行序列分配后预测域级系统的动力学和热力学行为,并检测到违反预期行为的情况。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83f4/6303802/4f83e360f829/rsif20180107-g1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验